CN116258287B - Reinforcing steel bar blanking combination optimization method - Google Patents
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Abstract
The invention relates to a reinforcement bar blanking combination optimization method based on BIM technology and artificial intelligence algorithm, which comprises the following steps: firstly, building an engineering entity model: building a structural model and a key part reinforcing steel bar model; rapidly extracting key information items required by the reinforcing steel bars through the word affix of the detail table; step two, data acquisition: taking segments with similar structural forms and similar construction types as templates, collecting data, archiving, cleaning and taking the segments as learning templates to perform deep learning of mathematical models; thirdly, database design and data cleaning: setting up 6 large database types, classifying all information, sorting and cleaning data through excel, and importing the data; fourth, constructing and calculating a mathematical model: and designing and adapting the entry of the information input by the reinforced bar raw material by adopting the python development language, and performing machine learning after operation.
Description
Technical Field
The invention relates to the technical field of artificial intelligence and rebar calculation, in particular to a rebar blanking combination optimization method based on a BIM technology and an artificial intelligence algorithm.
Background
The field of steel bar calculation is generally based on linear planning, and raw material estimation or theoretical value calculation is carried out on site according to steel bar blanking solely by means of an experienced master.
In the prior art, more one-dimensional linear programming methods, heuristic genetic algorithms, hybrid genetic algorithms, simulated annealing algorithms and the like are researched aiming at reinforcement optimization blanking.
The latter methods have high specialized requirements for reinforcing steel bar optimizing personnel, and are difficult to popularize in the actual application process of projects. The traditional manual optimization blanking method is greatly affected by human factors, has complicated process, and is difficult to ensure that the blanking optimization of the reinforcing steel bars can be very effectively carried out.
Disclosure of Invention
The invention aims to provide a BIM technology combined with an artificial intelligence algorithm based reinforcement blanking combination optimization method so as to solve the problems of how to reduce field material processing loss, improve dispatching accuracy and realize efficient construction.
The invention aims to solve the defects in the prior art and provides a reinforcement blanking combination optimization method based on BIM technology and artificial intelligence algorithm, which comprises the following steps:
firstly, building an engineering entity model:
building a structural model and a key part reinforcing steel bar model through Revit software; rapidly extracting key information items required by the reinforcing steel bars through the word affix of the detail table;
step two, data acquisition:
taking segments with similar structural forms and similar construction types as templates, collecting data, archiving, cleaning and taking the segments as learning templates to perform deep learning of mathematical models;
thirdly, database design and data cleaning:
setting 6 large database types, namely a coding database, an original database, a calculation model database, a reference efficiency database, a quota database and an influence factor database; finally classifying all the information, sorting and cleaning data through excel, and importing the data;
fourth, constructing and calculating a mathematical model:
and (3) using a keras standard mathematical model in tensorsurface as a benchmark, adopting python development language to design and adapt the entry of the information recorded by the steel bar raw material, and performing machine learning after operation.
Preferably, the key information items described in the first step include the bar model, shape change, bar length, number and/or type of the member to which they belong.
Preferably, the first step is to quickly implement the construction of the reinforcement matrix in a regular body using the function of reinforcement propagation.
Preferably, the coding database is used for defining a task unit and identifying a path, and takes the coding of an engineering quantity list sub item as a basis, a construction procedure is used as an object, and all data in the rebar calculation application are associated with the corresponding coding.
Preferably, the original database is used for storing all specific data of a task unit in a reinforcing steel bar procedure; the original database is built by a big data architecture and stored by a cloud server.
Preferably, the influencing factor database is used for storing influencing factors and selectable options of the steel bar construction process.
Preferably, the computing model database designs a corresponding computing model according to the characteristics of each 'task unit', and the computing model is trained, optimized, verified and stored in a solidifying way in an artificial intelligence mode.
Preferably, the reference efficiency database is used for storing a single worker efficiency data table and a single mechanical efficiency data table.
Preferably, the quota database is used for storing a single consumption quota table and a single mechanical shift yield quota table.
Preferably, tenforFlow Playground is utilized to graphically learn the presentation of the data model; the learning process and the finally optimized graphical effect can be clearly seen.
Advantageous effects
Compared with the prior art, the invention has the beneficial effects that:
the BIM technology and artificial intelligence algorithm combined steel bar blanking combination optimization method is a set of method researched by extracting actual on-site influence factor data (comprising loss, environmental influence, operator influence and the like), quickly finding out material combinations matched with the on-site by utilizing an artificial intelligence learning algorithm, and achieving the purposes of reducing on-site material processing loss, improving dispatching accuracy and high-efficiency construction.
The traditional method is manual calculation or mathematical algorithm calculation adopting linear programming, and only obtains the optimal solution of the proportion of the reinforcing steel bars according to the drawing, and the actual situation of the site is not considered.
The method for optimizing the steel bar blanking combination based on the BIM technology and the artificial intelligence algorithm is used for extracting actual influencing factors on site and finding out an optimal solution in the big data learning process. The data fed back by the on-site material record list shows that the traditional method often has the conditions of over-limit optimization, insufficient consumption and need of material supplement, and the material consumption deviation is about 2%. The invention can reduce the material consumption deviation to be within the interval of 0.2% by using an artificial intelligence algorithm, thereby being more in line with the site material and labor conditions.
The feature extraction element is firstly provided by the method of the invention, and is analyzed and judged through a large number of experience of construction managers, so that the required feature data entry is finally formed, and the feature data entry is also the core of the machine algorithm.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate and do not limit the invention.
FIG. 1 is a schematic diagram of the overall architecture of a database.
Detailed Description
The present invention is described in more detail below to facilitate an understanding of the present invention.
The reinforcement blanking combination optimization method based on BIM technology and artificial intelligence algorithm is developed and manufactured by applying the version of revit2022, python3 and Tensorflow 2.0.
The method for optimizing the steel bar blanking combination based on BIM technology and artificial intelligence algorithm comprises the following steps:
firstly, building an engineering entity model:
building a structural model and a key part reinforcing steel bar model through Revit software; key information items required for the quick extraction of reinforcing steel bars through the affix of the detail table include: the type of the steel bar, the deformation, the length and the number of the steel bars, the type of the member, etc.
In regular main bodies such as regular parts and standard sections, the invention can quickly realize the construction of the reinforcing steel bar model by utilizing the function of reinforcing steel bar transmission.
Step two, data acquisition:
in the project of one embodiment of the invention, the 1a section and the 1b section have similar structural forms and similar construction types, so the 1a section is taken as a template to collect data, and the data is filed and then cleaned and used as a learning template to carry out the material of the deep learning of the mathematical model. The results of the data acquisition are shown in tables 1 and 2.
TABLE 1 Steel bar Meter and position
Table 2, actual rebar amount comparison
Thirdly, database design and data cleaning:
the general framework of the invention sets up 6 large database types, namely a coding database, an original database, a calculation model database, a benchmark efficiency database, a quota database and an influence factor database. The architecture of the databases with respect to each other is shown in FIG. 1.
Encoding a database:
the coding database is used for defining a task unit and identifying a path, and takes the coding of an engineering quantity list sub item as a basis, a construction procedure as an object, and all data in the rebar computing application are associated with the corresponding coding.
Original database:
the original database is the overall storage of "task unit" specific data for the rebar process. The database is built by a big data architecture and stored by a cloud server.
Influence factor database:
the influencing factor database is used for storing influencing factors and selectable options of the steel bar construction process.
A calculation model database:
and designing a corresponding calculation model according to the characteristics of each 'task unit', training, optimizing, verifying and solidifying the calculation model in an artificial intelligence mode, and storing.
Reference efficiency database:
the reference efficiency database is used for storing a single worker efficiency data table and a single mechanical efficiency data table.
Quota database:
the quota database is used for storing a single consumption quota table and a single mechanical shift yield quota table.
And finally classifying all the information, and finishing and cleaning the data through excel to import the data.
Fourth, constructing and calculating a mathematical model:
the entry of the information recorded by the steel bar raw materials is designed and adapted by using a keras standard mathematical model in tensorsurface as a benchmark and adopting python development language, and machine learning can be performed after operation.
Graphic chemistry display of the data model is performed by using TenforFlow Playground; clearly, the learning process and the final optimized patterning effect can be seen.
The traditional method is manual calculation or mathematical algorithm calculation adopting linear programming, and only obtains the optimal solution of the proportion of the reinforcing steel bars according to the drawing, and the actual situation of the site is not considered.
The method of the invention is to extract the actual influencing factors of the site and find the optimal solution in the big data learning process.
The data fed back by the on-site material record list shows that the traditional method often has the conditions of over-limit optimization, insufficient consumption and need of material supplement, and the material consumption deviation is about 2%. The invention can reduce the material consumption deviation to be within the interval of 0.2% by using an artificial intelligence algorithm, thereby being more in line with the site material and labor conditions.
The feature extraction element is firstly provided by the method of the invention, and is analyzed and judged through a large number of experience of construction managers, so that the required feature data entry is finally formed, and the feature data entry is also the core of the machine algorithm.
The technical problems that the BIM technology combined with the artificial intelligence algorithm based steel bar blanking combination optimization method can solve include:
1. by utilizing the reinforcement propagation function of more than the revit2022 version, the reinforcement created on a certain type of member (such as a wall, a beam, a plate and a column) can be rapidly diffused into other constructions according to a self-defined rule, the problems of the creation of a reinforcement entity and the rapid derivation of the reinforcement amount are solved, and the main body of the reinforcement and the parameter index of the reinforcement characteristic can be transmitted according to the self definition.
The most reasonable raw material proportion of the steel bar size is calculated by combining the actual consumable condition of the construction site through artificial intelligence algorithm learning.
The calculation is divided into four steps, namely data preparation. Using the TensorFlow Datasets dataset, import into the TensorFlow machine learning framework. The specific commands are as follows:
import tensorflow.compat.v2 as tf
import tensorflow_datasets as tfds
# Construct a tf.data.Dataset
ds = tfds.load('mnist', split='train', shuffle_files=True)
# Build your input pipeline
ds=ds.shuffle(1024).batch(32).prefetch(tf.data.experimental.AUTOTUNE)for example in ds.take(1):
image, label = example["image"], example["label"]
the second step is to build a machine learning model.
Because the combined proportion of the raw materials of the reinforcing steel bars is set in the invention, the related field is linear programming in operation research, and a deep learning model is built by adopting KerasAPI in tensorf low. Because of the modularization characteristic, the model can be quickly built by using a neural network in a layer. The specific commands are as follows:
# lead-in model
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.callbacks import EarlyStopping, TensorBoard
import numpy as np
from datetime import datetime
gpu_ok = tf.test.is_gpu_available()
print(tf.__version__)
print(tf.keras.__version__)
print(gpu_ok)
Type layer stacking using tf.keras.sequential model
model = tf.keras.Sequential()
model.add(layers.Dense(32, activation='relu', input_dim=72))
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()
The third step is to deploy the model.
After the model is built, the learning flow of the model is configured by calling a combile method:
model = tf.keras.Sequential()
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.compile(optimizer=tf.keras.optimizers.Adam(0.001),
loss=tf.keras.losses.categorical_crossentropy,
metrics=[tf.keras.metrics.categorical_accuracy])
importing the data set into the model starts training.
dataset = tf.data.Dataset.from_tensor_slices((train_x, train_y))
dataset = dataset.batch(32)
dataset=dataset. Repeat () # prevents errors from being reported if the number of loops is greater than the total amount of data
val_dataset = tf.data.Dataset.from_tensor_slices((val_x, val_y))
val_dataset = val_dataset.batch(32)
val_dataset = val_dataset.repeat()
model.fit(dataset, epochs=10, steps_per_epoch=30,
validation_data=val_dataset, validation_steps=3)
The fourth step is evaluation and prediction.
Data were evaluated and predicted using tf.keras.model. Estimate and tf.keras.model. Predict methods:
model evaluation
test_x = np.random.random((1000, 72))
test_y = np.random.random((1000, 10))
model.evaluate(test_x, test_y, batch_size=32)
test_data = tf.data.Dataset.from_tensor_slices((test_x, test_y))
test_data = test_data.batch(32).repeat()
model.evaluate(test_data, steps=30)
Model # prediction
result = model.predict(test_x, batch_size=32)
print(result)。
The foregoing describes preferred embodiments of the present invention, but is not intended to limit the invention thereto. Modifications and variations to the embodiments disclosed herein may be made by those skilled in the art without departing from the scope and spirit of the invention.
Claims (1)
1. The method for optimizing the steel bar blanking combination based on the BIM technology and the artificial intelligence algorithm is characterized by comprising the following steps of:
firstly, building an engineering entity model:
building a structural model and a key part reinforcing steel bar model through Revit software; rapidly extracting key information items required by the reinforcing steel bars through the word affix of the detail table;
step two, data acquisition:
taking segments with similar structural forms and similar construction types as templates, collecting data, archiving, cleaning and taking the segments as learning templates to perform deep learning of mathematical models;
thirdly, database design and data cleaning:
setting 6 large database types, namely a coding database, an original database, a calculation model database, a reference efficiency database, a quota database and an influence factor database; finally classifying all the information, sorting and cleaning data through excel, and importing the data;
fourth, constructing and calculating a mathematical model:
designing and adapting the entry of the information recorded by the steel bar raw material by using a keras standard mathematical model in tensorsurface as a benchmark and adopting python development language, and performing machine learning after operation;
the coding database is used for defining a task unit and identifying a path, and takes the coding of a project quantity list subitem as a basis, a construction procedure as an object, and all data in the rebar calculation application are associated with the corresponding coding;
the original database is used for storing all specific data of a task unit in a reinforcing steel bar procedure; the original database is built by a big data architecture and stored by a cloud server;
the computing model database designs a corresponding computing model according to the characteristics of each task unit, and trains, optimizes, verifies and solidifies and stores the computing model in an artificial intelligence mode;
the reference efficiency database is used for storing a single worker efficiency data table and a single mechanical efficiency data table;
the quota database is used for storing a single consumption quota table and a single mechanical shift yield quota table;
the influence factor database is used for storing influence factors of the steel bar construction process;
firstly, constructing a reinforcing steel bar model in a regular main body by utilizing the function of reinforcing steel bar propagation; the regular body comprises a conventional part and a standard section;
graphic chemistry display of the data model is performed by using TenforFlow Playground; the learning process and the finally optimized graphical effect can be clearly seen;
the key information items in the first step comprise the types, the deformations, the lengths, the numbers and/or the types of the components of the reinforcing steel bars;
through artificial intelligence algorithm study, calculate most reasonable reinforcing bar size raw materials ratio in combination with the actual consumptive material condition of building site: the calculation is divided into four steps, namely data preparation is carried out firstly, and TensorFlow Datasets data sets are utilized to be imported into a TensorFlow machine learning framework; then, constructing a machine learning model, and constructing a deep learning model by adopting a KerasAPI in tensorsurface, or quickly constructing the deep learning model by using a neural network in a layer; then, a deep learning model is deployed, and after the deep learning model is built, a learning flow of the deep learning model is configured by calling a combole method; importing the data set into the deep learning model to start training; finally, the data were evaluated and predicted using the tf.keras.model. Estimate and tf.keras.model. Predict methods.
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| BIM技术在智能化钢筋加工中的探索;陆长松;;住宅与房地产(第23期);46-49 * |
| 基于MATLAB的钢筋下料优化算法;漏家俊;;建筑施工(第02期);154-156 * |
| 陆长松 ; .BIM技术在智能化钢筋加工中的探索.住宅与房地产.2018,(第23期),46-49. * |
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